Accurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep‐learning‐based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non‐linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple‐condition fusion network (MCF‐Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple‐source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple‐condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF‐Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF‐Net in applications of hydrogeological modeling.

China is facing a severe groundwater quality crisis amid economic development and climate change, yet the extent and trajectory of this crisis remain largely unknown. Here we developed a machine-learning model, incorporating natural and social-economic factors, to construct annual probabilistic maps of poor groundwater quality (PGQ, i.e., Class V based on the Chinese groundwater quality standard) across China from 1980 to 2100. Alarmingly, our findings indicate a concerning escalation in PGQ area ratio, rising from 17.3% in 1980 to 30.1% in 2000, and surging to 40.8% by 2020, adversely affecting 6.8%, 17.5%, and 36.0% of the Chinese population, respectively. The predominant drivers of this degradation were identified as agricultural discharge (contributing to 10.7% growth in PGQ area ratio), followed by groundwater exploitation (5.6%), industrial discharge (5.3%), domestic discharge (1.7%), climate change (0.5%), and land use change (-0.3%). By 2050, the PGQ area ratio could range from 37.9% to 48.3% under different socio-economic and climate scenarios. Our study highlights the urgent need for effective water resources management and conservation measures to mitigate the deteriorating trend of groundwater quality and address the challenges posed by socio-economic development and climate change, thereby safeguarding water security for China and the global community.